1. Field of the Invention
The present invention generally relates to data processing methods and apparatuses and, more particularly, to a data processing method and a data processing apparatus that allow noise contained in data to be easily and effectively eliminated.
2. Description of the Related Art
Normally, transmitted or read data, such as image data or sound data, contains time-varying noise. Conventionally, for eliminating noise contained in data, the whole input data may be averaged (hereinafter sometimes referred to as “overall average”), local input data may be averaged, which is referred to as “moving average”, or a given item of data may be substituted with a median of surrounding data. To reduce noise in images, for example, the following technique is known. One frame is used as a reference frame, and a motion vector of another frame is determined, thereby motion-compensating the second frame by using the motion vector. The weighted mean between the motion-compensated frame and the reference frame is then determined.
However, the above-described conventional techniques of eliminating noise present the following problems.
The technique of calculating the overall average is effective if the degree of noise contained in the data, i.e., the signal-to-noise (S/N) ratio, is constant. However, when the S/N ratio varies, data having a poor S/N ratio adversely influences data having a good S/N ratio, thereby making it difficult to effectively remove noise.
According to the technique of calculating moving averages, the average of data which is temporally close to input data is obtained, and thus, the processing result is susceptible to variations in the S/N ratio. That is, with input data having a high S/N ratio, the processing result also has a high S/N ratio. With input data having a low S/N ratio, the processing result also has a low S/N ratio.
According to the above-described average calculating techniques, the averaged data is smoothed. Accordingly, if this technique is used for eliminating noise in images, portions in which data sharply changes, i.e., sharp edges, are lost.
In the median-substituting technique, the temporal order of data is disturbed, which may seriously impair the characteristics of an original waveform. In using a motion vector, if the motion vector is erroneously detected, the quality of the processed image is considerably deteriorated.